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The structure of moods
Person. individ. LX% Vol. 13, No. 8, pp. 909-919, Printed in Great Britain. All rights reserved
1992 Copyright
0191-8869/92 $5.00 + 0.00 1992 Pergamon Press Ltd
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THE STRUCTURE OF MOODS CHRIS MCCONVILLE’ and COLIN COOPERS ‘Department of Psychology, University of Ulster, Coleraine, Co. Londonderry BT52 ISA and %hool of Psychology, The Queen’s University of Belfast, Belfast BT7 INN, N. Ireland (Received
4 October
1991)
Summary-An attempt was made to identify the main mood factors using items from widely used questionnaires. A hierarchical factor analysis of 170 mood items was performed. The items formed five primary mood factors (Depression, Hostility, Fatigue, Anxiety and Extraversion) with one general factor (hedonic tone). All the primaries correlated with this general factor. However, the adequacy of this structure was questioned on the grounds that some psychometric assumptions have been broken by most mood scale constructors. A posrhoc analysis of a subset of items showed two factors may better map the higher mood sphere.
In a clinical setting, valuable information on current feeling states can be gained from self-report mood scales. The bewildering variety of questionnaires measuring many supposedly distinct moods suggests little agreement on the most important mood dimensions. This point was addressed by Watson and Tellegen (1985) who stressed that assessments must reflect emotional experience. Thus began a systematic search to map the structure of moods comprehensively via self-reports. The important questions concern the utility of multiple mood instruments, the number and nature of discrete moods and the explanatory power of higher level mood factors. Recent mood research has been aimed at the identification of broad, parsimonious structures superordinate to the many discrete primary mood factors. Watson and Tellegen (1985) identified two major components of measurable mood variance which they termed positive and negative affect. Positive affect they described as a ‘zest for life’, whilst negative affect denoted a state of ‘unpleasant arousal’ or feeling ‘upset’. These two independent factors are said to account for up to two-thirds of the mood variance mapped by the primary mood factors. Alternative definitions of two-factor higher-order structures have been proposed. For example, Thayer (1986) has observed that some items loading highly on his General Activation and High Activation factors are also among the best markers of positive and negative affect, respectively (e.g. positive = ‘active’, ‘peppy’, ‘drowsy’, ‘sleepy’, ‘sluggish’; negative = ‘fearful’, ‘jittery’, ‘calm’, ‘at rest’, ‘placid’). Cooper and McConville (1989) identified positive affect as being factorially equivalent to the Curran and Cattell (1976) factor of state Extraversion, with negative affect equivalent to their factor of state Anxiety. Russell (1980) has proposed an alternative pair of axes rotated 45” from the Watson and Tellegen factors. These are termed Pleasantness-Unpleasantness and Engagement-Disengagement. High Engagement, located in factor space midway between high positive affect and high negative affect, contains marker items such as ‘aroused’, ‘astonished’ and ‘surprised’. The Pleasantness pole, located midway between high positive affect and low negative affect, has descriptors such as ‘content’, ‘happy’ and ‘satisfied’. In preference to a two-factor model, Williams (1989, 1990) suggests a single good/bad mood dimension reflects general mood. It was also the level of measurement chosen by Wessman and Ricks (1966) for their important early work on mood. To show the advantages of the positive and negative affect dimensions over alternative structural arrangements Watson and Tellegen reanalysed several studies. They found two factors for both adult and child samples, even when previous studies had located more. By achieving simple structure (maximizing the pure loadings on each factor), Watson and Tellegen believed they had improved on previous work, including Russell’s. Recently Burke, Brief, George, Roberson and Webster (1989) found the positive and negative affect dimensions split into four monopolar factors when mood was studied in the workplace. Their 909
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CHRIS MCCONVILLE and COLIN CARPER
confirmatory factor analysis suggests that the two-factor structure may not be as robust as at first thought. They rightly point out, however, that their twenty item scale could not sample the complete mood realm, which limits their conclusions. Nevertheless, they highlight the parallels between this structure and that identified by Thayer (1978) who reported two pairs of factors, one pair positively toned and the other negatively. Matthews, Jones and Chamberlain (1990) on the other hand claim that three factors account for the majority of mood variance. They criticise other studies for retaining the wrong number of factors when conducting factor analyses. However, their mood items were specifically selected to find three hypothesized dimensions, two of arousal and one of hedonic tone. Selecting items in this fashion presupposes that no other important dimensions exist. With no firm agreement on the nature of mood structures it is too early to narrow the sampling of mood variables. From a series of investigations into the higher-order structure of the Eight State Questionnaire (SSQ; Curren & Cattell, 1976) Boyle (1987a, 1987b, 1989a) found two main factors of state Extraversion and state Neuroticism. These were superordinate to the original eight factors and similar to positive and negative affect. However a major weakness of these studies is the unquestioning belief that the 8SQ items form a perfect replicable factor structure. Unfortunately, there is empirical evidence that the factors transformed to scales in the SSQ were not correctly identified in the first place (Curran, 1968; Barton, Cattell & Connor, 1972), making Boyle’s analyses questionable. Boyle’s (1987b, 1989a) studies are important, however, for demonstrating that the SSQ measures states and not traits. The differential R (dR) method of analysing mood data (Cattell, 1973) involves testing S’s twice and factor analysing the difference scores. An ordinary R factor technique based on a single session may pick up some trait variance, whereas the dR technique using change scores must surely be measuring transient states. Boyle’s (1987b) dR analysis shows two higher-order SSQ factors comparable to Watson and Tellegen’s (1985) two factors from crosssectional R analyses. Higher-order analysis of multiple mood states is a useful exercise when parsimony is importantas when devising brief but broad mood scales. However, to date these procedures have had methodological flaws. The work of Boyle is not especially helpful as the SSQ scales have doubtful discriminant validity (as indicated by the high scale intercorrelations). Watson and Tellegen appear closest with their series of item factorings. Yet it is not certain that the second-order factors are correlated with all the primary factors. Any primary factor (or significant portion of a factor) orthogonal to the second-order factors must be regarded as an important mood. If such unique variance exists it would alter how best to map affective space. It would also increase the amount of variance accounted for without sacrificing parsimony. Some researchers reject higher-order factors as an adequate summary of the measurable mood sphere. Howarth, for instance, uses ten different scales to provide maximum information (Howarth & Schokman-Gates, 1981). Many examples of multiple mood questionnaires exist, examples include the Howarth Mood Adjective Checklist (HMACL4; Howarth, 1988), the Profile of Mood States (POMS-BI; Lorr & McNair, 1988), the SSQ (Curran & Cattell, 1976) and the Differential Emotions Scale (DESIII; Izard, Dougherty, Bloxom & Kotsch, 1974). The HMACL and the DES111 both claim to measure ten mood states, the POMS claims to measure six and the 8SQ eight mood states, with some states unique to particular questionnaires. Dissatisfied with the clinical background of early personality questionnaires Cattell (1946) introduced the “personality sphere” concept to broaden item sampling. With the clinical background of many mood scales there is a strong tendency to measure negative moods. Recently Howarth and Zumbo (1989) expressed concern over the use of scales that are solely negatively oriented. Indeed a content analysis of psychology textbooks carried out by Carlson (1966) revealed twice as many references to negative moods as to positive ones. In response, Howarth developed a mood form that included some positive concepts such as Optimism and Cooperation (HMACL4). For this researcher parsimony is forsaken for a qualitatively richer source of emotional assessment. Another major problem is the tendency to measure more ‘moods’ than are observable in the data. Some multiple mood instruments measure subtle variations of the same theme, as indicated by high intercorrelations among some mood scales. For example, Howarth and Young (1986) note some large correlations among the ten scales of the HMACL4, whilst Matthews (1983) reports the mean
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correlation between the 8SQ scales as r = 0.59, uncorrected for unreliability. Correlations between scales become problematic when they are of such a magnitude that the scales are measuring much the same thing-for example Depression and Anxiety in the HMACL4 share 50% of variance (r = 0.71) (Howarth & Young, 1986). The structure of moods should be defined by a sample of items measuring many different facets of moods, it is not clear that this principle of psychometry is adhered to in many cases. Narrow item sampling appears to have blighted a lot of early research into moods. Cattell and Kline (1977) have stressed the importance of sampling the universe of variables when using factor analysis to identify the major personality or ability traits. Mood researchers, on the other hand, define the mood sphere with less precision, often preferring to measure moods identified from clinical research. For example the POMS; the Clyde Mood Scale (Clyde, 1963) and the Nowlis Mood Adjective Checklist, (Nowlis & Nowlis, 1956) were all developed in this manner-see Howarth and Schokman-Gates (198 1) for a review of multiple mood instruments. Defining various mood structures from clinical observation places limits on the variety of items sampled. To map the entire mood sphere it is necessary to remember that the reliability of a test assumes that the item sample forms a random sample of items in this domain. Selective sampling prevents proper estimates of reliability. It is not possible to factor such an item sample to determine the number and nature of the fundamental mood states. Highly homogeneous scales known as “bloated specifics” are the most likely result of this analysis (Cattell & Kline, 1977, p. 27). Thus, although several reliable mood scales have been developed, it is not clear that these map the whole domain of mood. The item sampling carried out by two-factor theorists is not exempt from similar criticism. In Watson and Tellegen’s extensive test of this structure there is a high degree of item overlap between the various studies reanalysed. This limits their conclusions by preventing the possibility of analysing the scales together. It also means that the duplicated items would be expected to form specific factors. Thus, what is found for one solution will, by necessity, be found in their other solutions. The present study attempts to improve on previous research by adopting the following item sampling criteria: (1) it includes a wide range of distinct items, avoiding the issue of overlap; and (2) the items are carefully chosen to represent three approaches to mood measurement. (a) Markers for positive and negative affect are present. (b) The POMS questionnaire has been included as a reliable and valid example of a multiple mood scale with a two-factor higher-order structure (Lorr & Wunderlich, 1988). (c) The SSQ is included as a multifactorial instrument with a supposed two-factor second-order structure. It is included primarily for its behaviourally reflective items as single item descriptors do not cover behavioural elements of moods. Rorer (1972) has queried the adequacy of single word adjectives or adverbs to describe the total personality domain. This doubt may be extended to the mood realm, hence the need to sample more than one type of item. The aims of the present study are threefold: (1) To replicate the second-order analyses (from scale scores) found by other authors. This places the present data into the general framework of previous research. (2) To identify mood factors in a sample of items from the major mood questionnaires, correcting for item overlap. (3) To identify any primary factors orthogonal to the higher-order factors. This has not been properly addressed. METHOD
Tests
A total of 170 commonly used mood items were chosen to investigate the structure of mood. The items were from the broadest published mood scales,-namely Anxiety, Stress, Depression, Regression, Guilt, Extraversion and Arousal scales of the SSQ and from Confusion-Clearheaded, Depression-Elation, Unsure-Confident, Tired-Energetic, and Hostile-Agreeable scales from the POMS. The purest markers of positive and negative affect dimensions from Zevon and Tellegen (1982) were also included. Each scale had a maximum of 12 items, positive affect was the smallest
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CHRIS MCCONVUE
and C~LIN CARPER
scale with 6 items. The 8SQ and POMS both have 4-point response formats, whilst positive and negative affect have 5-point rating scales. One way of approximating a random sample of mood items is to sample items from all major published mood scales. Between them they should cover all the most important mood items. However, some scales may occur in more than one questionnaire, and some scales within certain questionnaires may be so highly correlated as to make their independence doubtful. It is thus necessary to sift through these items to remove obvious synonyms, in much the same manner as Cattell (1946) when refining the “personality sphere”. Two such forms of sifting are used in the studies reported below. The first merely removes clear synonyms. The second is based on the results from factor analysing the first set of items. Should any factors have a disproportionately large number of items with salient loadings, some items are removed at random. This ensures that smaller, but meaningful, factors are not “swamped” by one or two large factors that might result from an initial biased sampling of mood items. To ensure proper item sampling the following measures were taken: (a) Only single occurrences of items were included in the analyses. For instance, the POMS scales contain 8 items that are duplicated in the positive and negative affect scales, rather than all 16 items only 1 of each duplicated pair was included. (b) Attempts were made to trim cases where too many items marked a particular construct. There are effectively 3 scales that measure anxiety (negative affect and 1 each from the POMS and SSQ) as well as empirical evidence that other scales are highly correlated with anxiety (e.g. see Matthews, 1983). The POMS Anxious-Composed scale was therefore arbitrarily excluded from further analyses, leaving 24 items that putatively measured anxiety. This step was necessary as too many items of a similar nature within an item pool take up variance at the expense of other meaningful factors (Comrey, 1973; Kline, 1979). Two Depression scales (24 items) have been included in the study, although when one Depression scale was excluded (SSQ) there were no differences in the factor structures between solutions. Subjects
The mood items were administered to 497 school pupils as part of a larger sample of personality and mood scales. The Ss came from ten different schools and were aged between 13 and 18 with an average age of 15.2 years. 230 were males and 266 were females (1 neglected to state gender). As there are no theoretical reasons to assume differences between the sexes, and Boyle (1989b) found no important sex differences with the SSQ scales, the data were analysed ignoring the sex of the respondent. Procedure
The mood scales were administered in the afternoon or late morning of the summer term after each school had finished its exams. Testing was carried out in groups ranging from 23 to 83 pupils. Initial instructions were read to the children but each new scale had its own directions that the children were asked to follow carefully. Younger children were allowed to place a question mark at any items they did not understand. In accord with Russell and Ridgeway (1983) several synonyms were given to some younger children to help explain the meaning of certain words. None of the mood items used in the analyses were responded to with question marks. Factor
analyses
Although most of the statistical techniques used here are fairly common procedures it is necessary to explain their inclusion over other methods. Two common techniques, the Scree Test (Cattell, 1966) and the MAP test (Velicer, 1976) were applied to determine the number of factors to retain. Both are useful in determining the number of factors to retain for rotation (Zwick & Velicer, 1982, 1986). Principal Components Analysis is employed for most of the succeeding analyses where higher-order structures are needed. As Loehlin (1987, p. 204) notes, statistical procedures such as maximum likelihood factor analysis are of doubtful worth when higher-order structures are sought, as these are influenced by sample sizes for first-order analyses. Maximum Likelihood Factor
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Extraction (Lawley & Maxwell, 1963) is applied in other areas of the present study. This extraction technique may be preferable to the iterative technique of Principal Factors Extraction as used in Boyle’s studies (Tabachnick & Fidell, 1989, pp. 626627). Choice of rotation is often based on ease of interpretation versus determining the relatedness between factors. For the former case orthogonal rotation is applied, the most common procedure being Varimax, used extensively by Watson and Tellegen. Oblique solutions, such as Direct Oblimin (Jennrich & Sampson, 1966), allow the researcher to perform analyses of the hierarchical structure of factors, they are widely used in this study. The most important analysis in the current study is the Hierarchical Factor Analysis (Schmid & Leiman, 1957). Firstly, several primary factors are derived through principal component extraction and Direct Oblimin rotation, the correlations between these factors are themselves factored as in ordinary second-order analyses. If these factors are correlated then they too are factored and so on, until either a general factor emerges or the factors are essentially uncorrelated. All higher-level factors are then partialled out of previous levels. This makes all the levels mutually uncorrelated. It is a distinct possibility that significant variance in one or more factors at lower levels may not be accounted for by the factors further along the hierarchy. This unexplained variance would represent important mood factors not captured in previous secondorder analyses.
RESULTS
Scale factorings
of the 8SQ: consistent findings?
The initial investigation involved a similar higher-order analysis of the 8SQ (and the other scales) to those of Boyle (1987b, 1989a). As was pointed out above, Boyle may have been uncritical in using the SSQ scales as templates for higher-order analyses. Indeed, Cattell’s (1973).Table 29 (pp. 214215) shows a less than perfect example of a stable factor structure (this is particularly unconvincing as only the marker items are shown). However, if our second-order factors are consistent with those found by Boyle then we can infer some validity from later results involving 1 items not constrained by scales of dubious worth. All 15 scales were included in the analysis, which is similar to Boyle’s process of conjoint factoring. A maximum likelihood factor analysis was carried out as we were not projecting the higher-orders onto the original variables. Four factors were extracted as indicated by the Scree Test (and in accord with Boyle). These were rotated to simple structure by Direct Oblimin. The four factors explained 71.8% of the scale variance, with 36% of the variables (scales) falling in the 0.1 hyperplane. The MAP test indicated 3 factors but this reduced the variables on the 0.1 hyperplane to 31%. The factor pattern matrix is summarized in Table 1. Of particular interest here is the close resemblance between this solution and those reported by Boyle (1987b, 1989a) for the SSQ factors. There are only two minor inconsistencies across the three studies concerning the Depression and Regression factors. Noticeably negative affect is found alongside the Anxiety factor and positive affect is on the same factor as Extraversion, as found by Cooper and McConville (1989). A principal axis factoring was also carried out on these data (the preferred method in Boyle’s work). Although four factors accounted for 79.3% of the scale variance and placed 40% of the variables in the 0.1 hyperplane all the 8SQ scales tended to load together on the first factor with some spreading between factors. This appeared to have a much less meaningful structure than the preferred maximum likelihood solution. The similarity in scale alignment between the two solutions holds despite a major difference in methodology. Boyle advocates the use of a dR factor analysis, factoring the difference scores between two occasions. This is believed to overcome the possible “trait contamination” of responses from the more common snapshot R technique. However, Table 1 shows three similar solutions, regardless of methodology. Note that Boyle’s (1989a) reanalysis of a previous study also employs an R factoring (although his table heading refers to difference scores), with little departure from the present results.
CHRIS MCCONVILLE and C~LIN COOPER
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Table 1. Factor analysis of 15 mood scales, with a comparison of the higher-order structure of the SSQ across three studies (in boxes) Present study (R factor analysis)
Factors
Factors 1st
3rd
2nd
4th
1st -
-0.47
Pos affect Neg affect
Boyle (1989a) (R factor analysis)
Boyle (1987b) (dR factor analysis)
Factors 1st
2nd
2nd
Anxiety stress Depression Regression Fatigue Guilt Extraversion Arousal Clearheaded Elated Confident Energetic Agreeable
Item factoring:
what are the main primary factors?
Having obtained some consistent results with past studies attention was turned to the identification of the main mood factors from the 170 mood items. A principal components analysis was performed on the items. Five factors were extracted as indicated by the Scree Test, and these were rotated using Direct Oblimin. The first 5 components accounted for 36.6% of the total item variance, with 40% of the variables falling in the 0.1 hyperplane. The MAP test indicated that 14 factors be extracted but this added very little to the interpretation. Increasing the number of factors from 5 to 14 increased the percentage of variance explained by only 12% (to 48.2%). As this tended to break the factors into smaller, less meaningful structures no further attention was given to this solution. All five factors were bipolar using both positively and negatively toned items in their structure. One factor was derived mainly from POMS items with a few from negative affect. This amounted to a factor of Depression. It contained all the depression adjectives from the POMS ElationDepression scale, but not from the elated end of the dimension (except the item ‘elated’). It also included many items relating to feelings of inadequacy and muddled thoughts, classic symptoms of depression (DSM-III, 1980). Another factor contained a collection of terms pertaining to Hostility with a negative usage of elation items, most items were from POMS scales. A third factor was made up of Energy-Fatigue terms from both the 8SQ and the POMS. An Anxiety factor was also present composed of a few negative affect items and many from the SSQ Anxiety, Stress, Depression, Guilt and Fatigue factors. The fifth factor loaded on some positive affect, 8SQ and POMS items. This could be termed an Extraversion factor, loading best on items relating to being adventurous and a liking for games and parties. Table 2 shows the factor correlation matrix with a range of correlations between 0.10 and 0.33. The five factors were then subjected to a second-order principal components analysis. Both the Scree Test and the MAP test indicated that one factor should be extracted. Figure 1 (the lines leading from each box to the second-order factor) shows that all five factors loaded fairly equally on this general factor (indicating a depressed, angry, tired, anxious and unsociable attribute), which could be described as a state of hedonic tone, the one positive factor related inversely to the other four. The first principal component accounted for 39.2% of the variance in the five primary factors.
Table 2. Factor correlation matrix for the five primary factors I 1. 2. 3. 4. 5.
Depression Hostility Fatigue Anxiety Extraversion
1.0 0.21 0.20 0.32 -0.10
2 1.0 0.33 0.25 -0.30
Factors 3
1.0 0.23 -0.30
4
1.0 -0.12
5
1.0
The structure
1
rt
Hostlllty
Depresslo” 4.44%
residual
3.20%
residual
of moods
Anxiety
Fatigue 3.15%
915
residual
3.10%
residual
Extroversion 3.28%
residwl
‘\\’
Fig. 1. Schematic representation of the mood hierarchy. The loadings of the five primary factors are shown projected on to the general factor. Also shown is the residual variance for each primary factor after removing the influence of the general factor.
The hierarchy:
are any primary factors
uncorrelated
with the general factor?
In the analysis up to this point only variance common to all the primaries has been extracted. The importance of the variance that has been discarded due to low correlations between the primaries and this general factor has yet to be determined. Partialling out the general factor from the five primaries makes it orthogonal to the remainder of the variance on each factor. This is important, because it is possible that the general factor could correlate very substantially with some primary factors but nearly zero with others. This would necessitate considering both the general factor and any primary factors that failed to load on it when assessing the broad dimensions of moods. Figure 1 indicates only minor independent effects for each of the 5 primary factors. Levels of residual variance ranged from 3.1% for anxiety to 4.4% for depression. The general factor absorbed 23.8% of the total variance for the five components originally extracted (36% was the original figure although rounding errors bring this to 40%, smaller data sets yield closer estimates). The place of positive
and negative
a#ect
in the structure
of moods
As a single ‘feeling-good’ vs ‘feeling-bad’ factor at the top of the hierarchy is not consistent with the two-factor theory this was investigated further by replicating a study by Cooper and McConville (1989). They used 41 items from state Anxiety, state Extraversion and positive and negative affect scales also used in the present study. Their Chain-P factor analysis found two main factors (state Anxiety and state Extraversion) that were essentially separable. The rationale for this replication concerns proper item and mood variable sampling. As only five factors have been uncovered at the primary level questions are raised about the independence of the supposedly distinct scales of the POMS and 8SQ-the former questionnaire purports to measure six moods and the latter eight. One possible reason for their apparent disintegration is that too many similar items were contained in scales with different names. The clinical nature of these terms was also reflected in the content of the individual primaries, with the final result being a single factor at the top of the hierarchy that was “swamped” by the anxiety types. This replication drastically cuts the number of items loading on the general factor to enable the state Extraversion and positive affect items to exert whatever influences they may have.
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CHRIS MCCONVILLEand COLIN CARPER
A principal components analysis was performed with three components extracted to echo the previous analysis on these items (the MAP test and Scree Test both indicated 3 factors with these data). These were then subjected to Direct Oblimin rotation. The 3 components claimed 36.4% of item variance, with 32.5% of the variables falling in the 0.1 hyperplane. The resultant factors of Anxiety, Extraversion and Anger were identical to those found by Cooper and McConville (1989). Anxiety and Anger were highly correlated in both studies (r = 0.56 here and r = 0.60 in the earlier study). Unlike the previous study a hierarchical analysis was performed on the three factors. A general factor of state Neuroticism was found that took up most of the variance in the Anger and Anxiety primaries, accounting for 22.7% of the total 36.4%. Interestingly, a sizeable portion of variance (7%; which is approx. 20% of the total item variance) remained in the Extraversion factor, which retained several large loadings. DISCUSSION
The present study attempted to identify the main self-report mood factors and any important primary factors independent of the higher-order factors. The results showed five primary factors, each contributing to the higher-order structure. The main analysis uncovered a basic structure of self-report mood of a single feeling-good/ feeling-bad dimension. This is similar to the hedonic tone dimension popular among early mood theorists (e.g. Wessman & Ricks, 1966). Unfortunately, it does not agree with the current view that two orthogonal factors best explain the major factor space. However, the adequacy of this one-factor structure is questionable in the light of the post hoc 41-item analysis. Here the two-factor structure is found among a selection of the items. This latter analysis differed from the 170-item analysis by employing a more even distribution of state Anxiety and Extraversion items (negative and positive affect, respectively). Indeed, the hierarchical analysis of the 170 items revealed a dominant role for ‘anxious’ type items, suggesting that the one-factor result may be due to sampling effects. Although care was taken to attempt to cover a broad spectrum of mood terms, there may not have been enough positive affect items. Bipolar factors of dimensions such as Anxiety are common .but few tap the state of Sociability. This may explain how the 170 items formed a hierarchy topped by a single hedonic tone dimension. : ’ Supposedly distinct mood scales appear to contain too many similar items. Matthews (1983) has shown evidence for this by reporting high intercorrelations between the SSQ scales, as has the present study-the general factor was composed of a mixture of Stress, Depression, Anxiety and other factors. Similarly, the discriminant validity of some of the POMS scales is doubtful (Norcross, Guadagnoli & Prochaska, 1984). Obviously the clinical importance of Stress, Anxiety and Depression factors outweighs the usefulness of sociable (extraverted) states, biasing most scales toward these clinically observed concepts. The loadings on the general factor show it to be a hedonic tone dimension. Some of the best markers are: “I’m in a playful, joyful mood” (0.726), “I feel so down I wonder if I can make it through the day” (-0.725) “I’m in a nice, comfortable mood” (0.718), “At the moment I feel in tip-top shape, both mentally and physically” (0.697), “Active” (0.670), “Energetic” (0.652), “Gloomy” (-0.640), with the highest loadings, generally, from the 8SQ items. Several markers of the Watson and Tellegen and Russell factors were included here but neither factorial alternative could be clearly identified. Both sets of factors had items loading highly on the general factor, but equally both had some marker items that did not. Steps were taken at the onset of the analysis to prevent any state being over-represented, which led to the removal of one anxiety scale. This was useful at the primary level but evidently less so at the highest level. The more even distribution of items in the 41-item analysis indicated a clearer picture of self-reported mood. Here both the Anxiety and Extraversion states showed up clearly and distinctly. The present results are consistent with previous literature, the 41-item analysis and the scale factorings both satisfactorily replicated earlier findings. Similar second orders from the identically forced primary factors in this study and those of Boyle (1987b, 1989a) suggests that the results obtained with items would be found in other studies. It is also highly likely that the 8SQ factors were wrongly identified in the first instance. This is evident from the reports of high intercorrela-
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tions between the eight scales as reported by Matthews (1983) but also by Boyle (1987a, Table 2). The five primary factors from the present results show much more moderate intercorrelations, thereby suggesting that some reported mood factors are less than perfectly discriminating. A possible criticism concerns the young age of the respondents in this study (mean age = 15.2). However, there is little to suggest that the range of scores from an adolescent sample does not approximate the distribution within the population. Lorr and Wunderlich (1988) administered the POMS to high school pupils whose average age was 15.6 and found a similar factor structure to that of adult samples. Similarly, Kotsch, Gerbing and Schwartz (1982) and Russell and Ridgeway (1983) used samples of children from various age groups with no departure from results of similar studies using adult samples. Therefore it is difficult to see how the present results would have been adversely affected by age. It might be argued that the items have been grouped together solely on their semantic relatedness. However, as the SSQ items are behaviourally anchored they would not necessarily follow this pattern, although adjectives might. It is more likely that the factors are valid, having used more than one item type. Additional evidence for the validity of the factors of positive and negative affect is given by Watson, Clark and Tellegen (1988). The questionable validity of the one-factor solution suggests that further work is needed to confidently map the higher-order mood sphere. Weeding out the redundant mood concepts will help to refine item samples. For instance, what is the difference between anxiety and stress? Evidently, very little difference can be determined (Matthews, 1983), yet scales exist attempting to differentiate between them. It has been suggested that mood scales do not suffer poor discriminant validity just because they happen to be highly correlated. Instead the preferred belief is that some moods occur together, as might be expected for negative states. Izard (1972) suggests that one mood elicits other related moods, which is why the high intercorrelations exist. If there were no correlations between moods higher-order analyses would not be possible and the mood sphere< would be mapped by the primary factors. Mood states are not reported in isolation and all’ have at least one other mood factor with which they correlate. Thus higher-order analys$ can be conducted to find the influence that produces this correlation (Cattell, 1973, p. 107’). If the intercorrelations are inflated through poor item sampling the resulting higher-orders.,may not be meaningful. The intercorrelations of the five primary factors in the present study are lower than others previously cited. These five factors loaded equally well on the general factor, indicating an important influence for this factor in self-reported mood. More sociability type items may have made a contribution to another higher-order factor as indicated by the 41-item analysis. Investigators need to be aware of their particular biases (clinical or otherwise) in choosing which moods to measure. Angleitner, John and Lohr (1986) pinpointed “borrowing” (p. 66) of items from earlier scales and the “idiosyncratic preconceptions” (p. 67) of researchers as recipes for seriously defective scales: defects, they stress, that cannot be repaired by factor or item analysis. The most likely outcome is highly homogeneous, bloated specific, scales, and scales that are simply too alike to be of any value. If major distinctions exist between states then they should be measured accurately. It is imperative that thorough sampling is carried out, to echo Kline (1986) “a test can be no better than its items” (p. 24).
CONCLUSIONS (1) From questionnaire measures of mood five primary factors were. found necessary to map affective space. These are Depression, Hostility, Energy, Anxiety and Extraversion. (2) One higher-order factor of hedonic tone (good vs bad mood) was found to influence all the primary factors. (3) A further analysis of a subset of items revealed a possible second orthogonal second-order factor of state Extraversion. This suggested that a two-factor structure of mood may be the most parsimonious account. Poor variable samples are proposed as a’major flaw in the currently available mood instruments.
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REFERENCES Angleitner, A., John, 0. P. & Lohr, F.-J. (1986). It’s what you ask and how you ask it: An itemmetric analysis of personality questionnaires. In Angleitner, A. & Wiggins, J. S. (Eds), Personality assessmenf via questionnaires. Berlin: SpringerVerlag. Barton, K., Cattell, R. B. & Connor, D. V. (1972). The identification of state factors through P-technique factor analysis. Journal of Clinical Psychology, 28, 459-463. Boyle, G. J. (1987a). Typological mood-state factors measured in the Eight State Questionnaire. Personality and Individual Differences, 8, 137-140. Boyle, G. J. (1987b). A conjoint dR-factoring of the 8SQ/DES-IV multivariate mood-state scales. Australian Journal of Psychology, 39, 79-87. Boyle, G. J. (1989a). Factor structure of the Differential Emotions Scale and the Eight State Questionnaire revisited. The Irish Journal of Psychology, 10, 5666. Boyle, G. J. (1989b). Sex differences in reported mood states. Personality and Individual Differences, IO, 1179-I 183. Burke, M. J., Brief, A. P., George, J. M., Roberson, L. & Webster J. (1989). Measuring affect at work: Confirmatory analyses of competing mood structures with conceptual linkage to cortical regulatory systems. Journal of Personality and Social Psychology, 57, 1091-l 102. Carlson, E. R. (1966). The affective tone of psychology. Journal of General Psychology, 75, 65-78. Cattell, R. B. (1946). The description and measurement of personality. London: Harrap. Cattell, R. B. (1966). The scree test for the number of factors. Multiuariafe Behavioral Research, I, 245-276. Cattell, R. B. (1973). Personality and mood by questionnaire. London: Jossey Bass. Cattell, R. B. & Kline P. (1977). The scien@c analysis of personality and motivation. London: Academic Press. Clyde, D. J. (1963). Clyde Mood Scale. University of Miami, Biometrics lab., Coral Gables, FL. Comrey A. L. (1973). A first course in factor analysis. New York: Academic Press. Cooper, C. & McConville, C. (1989). The factorial equivalence of state anxiety-negative affect and state extraversionpositive affect. Personality and Individual Differences, IO, 919-920. Curran, J. P. (1968). Dimensions of state change in Q-data and chain P-technique on twenty women. Unpublished Masters thesis. University of Illinois, Urbana. Curran, J. P. & Cattell, R. B. (1976). Manual for the Eight Stare Questionnaire. Champaign, IL: Institute for Personality and Ability Testing. DSM-III (1980). Diagnostic and statistical manual of mental disorders. (3rd Edn). American Psychiatric Association. Howarth, E. (1988). Mood differences between the four Galen personality types: Choleric, Sanguine, Phlegmatic, Melancholic. Personalify and Individual Differences, 9, 173-175. Howarth, E. & Schokman-Gates, K. L. (1981). Self-report multiple mood instruments. British Journal of Psychology, 72, 421-441. Howarth, E. & Young, P. D. (1986). Patterns of mood change. Personality and Individual Differences, 7, 275-281. Howarth, E. & Zumbo, B. D. (1989). An empirical investigation of Eysenck’s typology. Journal of Research in Personality, 23, 343-353. Izard, C. E. (1972). Patterns of emotion. New York: Academic Press. Izard, C. E., Dougherty, F. E., Bloxom, B. M. & Kotsch, N. E. (1974). The Differential Emotions Scale: A method of measuring the subjective experience of discrete emotions. Vanderbilt University Department of Psychology, Nashville. Jennrich, R. I. & Sampson, P. F. (1966). Rotation for simple loadings. Psychomefrika, 31, 313-323. Kline, P. (1979). Psychometrics and psychology. London: Academic Press. Kline, P. (1986). A handbook of test conslrucfion. London: Methuen. Kotsch, W. E., Gerbing, D. W. & Schwartz, L. E. (1982). The construct validity of the Differential Emotions Scale as adapted for children and adolescents. In Izard, C. E. (Ed.), Measuring emotions in infanrs and children (pp. 251-278). New York: Cambridge University Press. Lawley, D. N. & Maxwell, A. E. (1963). Factor analysis as a statistical method. London, Butterworth. Loehlin, J. C. (1987). Latent variable models: An introduction to factor, parh and structural analysis. Hillsdale, NJ: Lawrence Erlbaum Associates. Lorr, M. & McNair, D. M. (1988). Manual, Profile of Mood States. Bipolar Form. Educational and Industrial Testing Service, San Diego, CA. Lorr, M. & Wunderlich, R. A. (1988). Self-esteem and negative affect. Journal of Clinical Psychology, 44, 36-39. Matthews, G. (1983). Personality, arousal states and intellectual performance. Unpublished doctoral dissertation, University of Cambridge. Matthews, G., Jones, D. M. &Chamberlain, A. G. (1990). Refining the measurement of mood: The UWIST Mood Adjective Checklist. British Journal of Psychology, 81, 17-42. Norcross, J. C., Guadagnoli, E. & Prochaska, J. 0. (1984). Factor structure of the Profile of Mood States (POMS): Two partial replications. Journal of Clinical Psychology 40, 1270-1277. Nowlis, J. & Nowlis, H. (1956). The description and analysis of mood. Annals of the New York Academy of Science, 55, 345-355. Rorer, L. G. (1972). ‘The Adjective Checklist’. In Buros, 0. K. (Ed.) Seventh mental measurements yearbook. Highland Park: Gryphon. Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39, 1161-l 178. Russell, J. A. & Ridgeway, D. (1983). Dimensions underlying children’s emotion concepts. Developmental Psychology, 19, 795-804. Schmid, J. & Leiman, J. M. (1957). The development of hierarchical factor solutions. Psychometrika 22, 53-61. Tabachnick B. G. & Fidel1 L. S. (1989). Using multivariate statistics (2nd Edn). New York: Harper 8r Row. Thayer, R. E. (1978). Factor analytic and reliability studies on the Activation-Deactivation Adjective Checklist. Psychological Reports, 42, 747-756. Thayer, R. E. (1986). Activation-Deactivation Adjective Checklist: Current overview and structural analysis. Psychological Reports, 58, 607614.
The structure of moods Velicer, W. F. (1976). Determining the number of components from the matrix of partial correlations. Psychometrika,
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321-327.
Watson, D. & Tellegen, A. (1985). Toward a consensual structure of mood. Psychological Bulletin, 98, 219-235. Watson, D., Clark, L. A. & Tellegen, A. (1988). Development and validation of brief measures of Positive and Negative Affect: The PANAS scales. Journal of Personality and Social Psychology, 54, 1063-1070. Wessman, A. E. & Ricks, D. F. (1966). -Mood and personality. New York:. Holt, Rinehart & Winston. Williams. D. G. (1989). Personalitv effects in current mood: Pervasive or reactive? Personality and Individual Differences, __ IO, 941-948. ~ ’ Williams, D. G. (1990). Effects of Psychoticism, Extraversion, and Neuroticism in current mood: A statistical review of six studies. Personality and Individual Differences, I I, 615-630. Zevon, M. A. & Tellegen, A. (1982). The structure of mood change: an idiographic/nomothetic analysis. Journal of Personality and Social Psychology, 43, 11l-122. Zwick, W. R. & Velicer, W. F. (1982). Factors influencing four rules for determining the number of components to retain. Multivariate
Behavioral
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Zwick, W. R. & Velicer, W. F. (1986). Comparison of five rules for determining the number of components to retain. Psychological
Bulletin, 99, 432-442.
1992 Copyright
0191-8869/92 $5.00 + 0.00 1992 Pergamon Press Ltd
0
THE STRUCTURE OF MOODS CHRIS MCCONVILLE’ and COLIN COOPERS ‘Department of Psychology, University of Ulster, Coleraine, Co. Londonderry BT52 ISA and %hool of Psychology, The Queen’s University of Belfast, Belfast BT7 INN, N. Ireland (Received
4 October
1991)
Summary-An attempt was made to identify the main mood factors using items from widely used questionnaires. A hierarchical factor analysis of 170 mood items was performed. The items formed five primary mood factors (Depression, Hostility, Fatigue, Anxiety and Extraversion) with one general factor (hedonic tone). All the primaries correlated with this general factor. However, the adequacy of this structure was questioned on the grounds that some psychometric assumptions have been broken by most mood scale constructors. A posrhoc analysis of a subset of items showed two factors may better map the higher mood sphere.
In a clinical setting, valuable information on current feeling states can be gained from self-report mood scales. The bewildering variety of questionnaires measuring many supposedly distinct moods suggests little agreement on the most important mood dimensions. This point was addressed by Watson and Tellegen (1985) who stressed that assessments must reflect emotional experience. Thus began a systematic search to map the structure of moods comprehensively via self-reports. The important questions concern the utility of multiple mood instruments, the number and nature of discrete moods and the explanatory power of higher level mood factors. Recent mood research has been aimed at the identification of broad, parsimonious structures superordinate to the many discrete primary mood factors. Watson and Tellegen (1985) identified two major components of measurable mood variance which they termed positive and negative affect. Positive affect they described as a ‘zest for life’, whilst negative affect denoted a state of ‘unpleasant arousal’ or feeling ‘upset’. These two independent factors are said to account for up to two-thirds of the mood variance mapped by the primary mood factors. Alternative definitions of two-factor higher-order structures have been proposed. For example, Thayer (1986) has observed that some items loading highly on his General Activation and High Activation factors are also among the best markers of positive and negative affect, respectively (e.g. positive = ‘active’, ‘peppy’, ‘drowsy’, ‘sleepy’, ‘sluggish’; negative = ‘fearful’, ‘jittery’, ‘calm’, ‘at rest’, ‘placid’). Cooper and McConville (1989) identified positive affect as being factorially equivalent to the Curran and Cattell (1976) factor of state Extraversion, with negative affect equivalent to their factor of state Anxiety. Russell (1980) has proposed an alternative pair of axes rotated 45” from the Watson and Tellegen factors. These are termed Pleasantness-Unpleasantness and Engagement-Disengagement. High Engagement, located in factor space midway between high positive affect and high negative affect, contains marker items such as ‘aroused’, ‘astonished’ and ‘surprised’. The Pleasantness pole, located midway between high positive affect and low negative affect, has descriptors such as ‘content’, ‘happy’ and ‘satisfied’. In preference to a two-factor model, Williams (1989, 1990) suggests a single good/bad mood dimension reflects general mood. It was also the level of measurement chosen by Wessman and Ricks (1966) for their important early work on mood. To show the advantages of the positive and negative affect dimensions over alternative structural arrangements Watson and Tellegen reanalysed several studies. They found two factors for both adult and child samples, even when previous studies had located more. By achieving simple structure (maximizing the pure loadings on each factor), Watson and Tellegen believed they had improved on previous work, including Russell’s. Recently Burke, Brief, George, Roberson and Webster (1989) found the positive and negative affect dimensions split into four monopolar factors when mood was studied in the workplace. Their 909
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CHRIS MCCONVILLE and COLIN CARPER
confirmatory factor analysis suggests that the two-factor structure may not be as robust as at first thought. They rightly point out, however, that their twenty item scale could not sample the complete mood realm, which limits their conclusions. Nevertheless, they highlight the parallels between this structure and that identified by Thayer (1978) who reported two pairs of factors, one pair positively toned and the other negatively. Matthews, Jones and Chamberlain (1990) on the other hand claim that three factors account for the majority of mood variance. They criticise other studies for retaining the wrong number of factors when conducting factor analyses. However, their mood items were specifically selected to find three hypothesized dimensions, two of arousal and one of hedonic tone. Selecting items in this fashion presupposes that no other important dimensions exist. With no firm agreement on the nature of mood structures it is too early to narrow the sampling of mood variables. From a series of investigations into the higher-order structure of the Eight State Questionnaire (SSQ; Curren & Cattell, 1976) Boyle (1987a, 1987b, 1989a) found two main factors of state Extraversion and state Neuroticism. These were superordinate to the original eight factors and similar to positive and negative affect. However a major weakness of these studies is the unquestioning belief that the 8SQ items form a perfect replicable factor structure. Unfortunately, there is empirical evidence that the factors transformed to scales in the SSQ were not correctly identified in the first place (Curran, 1968; Barton, Cattell & Connor, 1972), making Boyle’s analyses questionable. Boyle’s (1987b, 1989a) studies are important, however, for demonstrating that the SSQ measures states and not traits. The differential R (dR) method of analysing mood data (Cattell, 1973) involves testing S’s twice and factor analysing the difference scores. An ordinary R factor technique based on a single session may pick up some trait variance, whereas the dR technique using change scores must surely be measuring transient states. Boyle’s (1987b) dR analysis shows two higher-order SSQ factors comparable to Watson and Tellegen’s (1985) two factors from crosssectional R analyses. Higher-order analysis of multiple mood states is a useful exercise when parsimony is importantas when devising brief but broad mood scales. However, to date these procedures have had methodological flaws. The work of Boyle is not especially helpful as the SSQ scales have doubtful discriminant validity (as indicated by the high scale intercorrelations). Watson and Tellegen appear closest with their series of item factorings. Yet it is not certain that the second-order factors are correlated with all the primary factors. Any primary factor (or significant portion of a factor) orthogonal to the second-order factors must be regarded as an important mood. If such unique variance exists it would alter how best to map affective space. It would also increase the amount of variance accounted for without sacrificing parsimony. Some researchers reject higher-order factors as an adequate summary of the measurable mood sphere. Howarth, for instance, uses ten different scales to provide maximum information (Howarth & Schokman-Gates, 1981). Many examples of multiple mood questionnaires exist, examples include the Howarth Mood Adjective Checklist (HMACL4; Howarth, 1988), the Profile of Mood States (POMS-BI; Lorr & McNair, 1988), the SSQ (Curran & Cattell, 1976) and the Differential Emotions Scale (DESIII; Izard, Dougherty, Bloxom & Kotsch, 1974). The HMACL and the DES111 both claim to measure ten mood states, the POMS claims to measure six and the 8SQ eight mood states, with some states unique to particular questionnaires. Dissatisfied with the clinical background of early personality questionnaires Cattell (1946) introduced the “personality sphere” concept to broaden item sampling. With the clinical background of many mood scales there is a strong tendency to measure negative moods. Recently Howarth and Zumbo (1989) expressed concern over the use of scales that are solely negatively oriented. Indeed a content analysis of psychology textbooks carried out by Carlson (1966) revealed twice as many references to negative moods as to positive ones. In response, Howarth developed a mood form that included some positive concepts such as Optimism and Cooperation (HMACL4). For this researcher parsimony is forsaken for a qualitatively richer source of emotional assessment. Another major problem is the tendency to measure more ‘moods’ than are observable in the data. Some multiple mood instruments measure subtle variations of the same theme, as indicated by high intercorrelations among some mood scales. For example, Howarth and Young (1986) note some large correlations among the ten scales of the HMACL4, whilst Matthews (1983) reports the mean
The structure of moods
911
correlation between the 8SQ scales as r = 0.59, uncorrected for unreliability. Correlations between scales become problematic when they are of such a magnitude that the scales are measuring much the same thing-for example Depression and Anxiety in the HMACL4 share 50% of variance (r = 0.71) (Howarth & Young, 1986). The structure of moods should be defined by a sample of items measuring many different facets of moods, it is not clear that this principle of psychometry is adhered to in many cases. Narrow item sampling appears to have blighted a lot of early research into moods. Cattell and Kline (1977) have stressed the importance of sampling the universe of variables when using factor analysis to identify the major personality or ability traits. Mood researchers, on the other hand, define the mood sphere with less precision, often preferring to measure moods identified from clinical research. For example the POMS; the Clyde Mood Scale (Clyde, 1963) and the Nowlis Mood Adjective Checklist, (Nowlis & Nowlis, 1956) were all developed in this manner-see Howarth and Schokman-Gates (198 1) for a review of multiple mood instruments. Defining various mood structures from clinical observation places limits on the variety of items sampled. To map the entire mood sphere it is necessary to remember that the reliability of a test assumes that the item sample forms a random sample of items in this domain. Selective sampling prevents proper estimates of reliability. It is not possible to factor such an item sample to determine the number and nature of the fundamental mood states. Highly homogeneous scales known as “bloated specifics” are the most likely result of this analysis (Cattell & Kline, 1977, p. 27). Thus, although several reliable mood scales have been developed, it is not clear that these map the whole domain of mood. The item sampling carried out by two-factor theorists is not exempt from similar criticism. In Watson and Tellegen’s extensive test of this structure there is a high degree of item overlap between the various studies reanalysed. This limits their conclusions by preventing the possibility of analysing the scales together. It also means that the duplicated items would be expected to form specific factors. Thus, what is found for one solution will, by necessity, be found in their other solutions. The present study attempts to improve on previous research by adopting the following item sampling criteria: (1) it includes a wide range of distinct items, avoiding the issue of overlap; and (2) the items are carefully chosen to represent three approaches to mood measurement. (a) Markers for positive and negative affect are present. (b) The POMS questionnaire has been included as a reliable and valid example of a multiple mood scale with a two-factor higher-order structure (Lorr & Wunderlich, 1988). (c) The SSQ is included as a multifactorial instrument with a supposed two-factor second-order structure. It is included primarily for its behaviourally reflective items as single item descriptors do not cover behavioural elements of moods. Rorer (1972) has queried the adequacy of single word adjectives or adverbs to describe the total personality domain. This doubt may be extended to the mood realm, hence the need to sample more than one type of item. The aims of the present study are threefold: (1) To replicate the second-order analyses (from scale scores) found by other authors. This places the present data into the general framework of previous research. (2) To identify mood factors in a sample of items from the major mood questionnaires, correcting for item overlap. (3) To identify any primary factors orthogonal to the higher-order factors. This has not been properly addressed. METHOD
Tests
A total of 170 commonly used mood items were chosen to investigate the structure of mood. The items were from the broadest published mood scales,-namely Anxiety, Stress, Depression, Regression, Guilt, Extraversion and Arousal scales of the SSQ and from Confusion-Clearheaded, Depression-Elation, Unsure-Confident, Tired-Energetic, and Hostile-Agreeable scales from the POMS. The purest markers of positive and negative affect dimensions from Zevon and Tellegen (1982) were also included. Each scale had a maximum of 12 items, positive affect was the smallest
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CHRIS MCCONVUE
and C~LIN CARPER
scale with 6 items. The 8SQ and POMS both have 4-point response formats, whilst positive and negative affect have 5-point rating scales. One way of approximating a random sample of mood items is to sample items from all major published mood scales. Between them they should cover all the most important mood items. However, some scales may occur in more than one questionnaire, and some scales within certain questionnaires may be so highly correlated as to make their independence doubtful. It is thus necessary to sift through these items to remove obvious synonyms, in much the same manner as Cattell (1946) when refining the “personality sphere”. Two such forms of sifting are used in the studies reported below. The first merely removes clear synonyms. The second is based on the results from factor analysing the first set of items. Should any factors have a disproportionately large number of items with salient loadings, some items are removed at random. This ensures that smaller, but meaningful, factors are not “swamped” by one or two large factors that might result from an initial biased sampling of mood items. To ensure proper item sampling the following measures were taken: (a) Only single occurrences of items were included in the analyses. For instance, the POMS scales contain 8 items that are duplicated in the positive and negative affect scales, rather than all 16 items only 1 of each duplicated pair was included. (b) Attempts were made to trim cases where too many items marked a particular construct. There are effectively 3 scales that measure anxiety (negative affect and 1 each from the POMS and SSQ) as well as empirical evidence that other scales are highly correlated with anxiety (e.g. see Matthews, 1983). The POMS Anxious-Composed scale was therefore arbitrarily excluded from further analyses, leaving 24 items that putatively measured anxiety. This step was necessary as too many items of a similar nature within an item pool take up variance at the expense of other meaningful factors (Comrey, 1973; Kline, 1979). Two Depression scales (24 items) have been included in the study, although when one Depression scale was excluded (SSQ) there were no differences in the factor structures between solutions. Subjects
The mood items were administered to 497 school pupils as part of a larger sample of personality and mood scales. The Ss came from ten different schools and were aged between 13 and 18 with an average age of 15.2 years. 230 were males and 266 were females (1 neglected to state gender). As there are no theoretical reasons to assume differences between the sexes, and Boyle (1989b) found no important sex differences with the SSQ scales, the data were analysed ignoring the sex of the respondent. Procedure
The mood scales were administered in the afternoon or late morning of the summer term after each school had finished its exams. Testing was carried out in groups ranging from 23 to 83 pupils. Initial instructions were read to the children but each new scale had its own directions that the children were asked to follow carefully. Younger children were allowed to place a question mark at any items they did not understand. In accord with Russell and Ridgeway (1983) several synonyms were given to some younger children to help explain the meaning of certain words. None of the mood items used in the analyses were responded to with question marks. Factor
analyses
Although most of the statistical techniques used here are fairly common procedures it is necessary to explain their inclusion over other methods. Two common techniques, the Scree Test (Cattell, 1966) and the MAP test (Velicer, 1976) were applied to determine the number of factors to retain. Both are useful in determining the number of factors to retain for rotation (Zwick & Velicer, 1982, 1986). Principal Components Analysis is employed for most of the succeeding analyses where higher-order structures are needed. As Loehlin (1987, p. 204) notes, statistical procedures such as maximum likelihood factor analysis are of doubtful worth when higher-order structures are sought, as these are influenced by sample sizes for first-order analyses. Maximum Likelihood Factor
The structure of moods
913
Extraction (Lawley & Maxwell, 1963) is applied in other areas of the present study. This extraction technique may be preferable to the iterative technique of Principal Factors Extraction as used in Boyle’s studies (Tabachnick & Fidell, 1989, pp. 626627). Choice of rotation is often based on ease of interpretation versus determining the relatedness between factors. For the former case orthogonal rotation is applied, the most common procedure being Varimax, used extensively by Watson and Tellegen. Oblique solutions, such as Direct Oblimin (Jennrich & Sampson, 1966), allow the researcher to perform analyses of the hierarchical structure of factors, they are widely used in this study. The most important analysis in the current study is the Hierarchical Factor Analysis (Schmid & Leiman, 1957). Firstly, several primary factors are derived through principal component extraction and Direct Oblimin rotation, the correlations between these factors are themselves factored as in ordinary second-order analyses. If these factors are correlated then they too are factored and so on, until either a general factor emerges or the factors are essentially uncorrelated. All higher-level factors are then partialled out of previous levels. This makes all the levels mutually uncorrelated. It is a distinct possibility that significant variance in one or more factors at lower levels may not be accounted for by the factors further along the hierarchy. This unexplained variance would represent important mood factors not captured in previous secondorder analyses.
RESULTS
Scale factorings
of the 8SQ: consistent findings?
The initial investigation involved a similar higher-order analysis of the 8SQ (and the other scales) to those of Boyle (1987b, 1989a). As was pointed out above, Boyle may have been uncritical in using the SSQ scales as templates for higher-order analyses. Indeed, Cattell’s (1973).Table 29 (pp. 214215) shows a less than perfect example of a stable factor structure (this is particularly unconvincing as only the marker items are shown). However, if our second-order factors are consistent with those found by Boyle then we can infer some validity from later results involving 1 items not constrained by scales of dubious worth. All 15 scales were included in the analysis, which is similar to Boyle’s process of conjoint factoring. A maximum likelihood factor analysis was carried out as we were not projecting the higher-orders onto the original variables. Four factors were extracted as indicated by the Scree Test (and in accord with Boyle). These were rotated to simple structure by Direct Oblimin. The four factors explained 71.8% of the scale variance, with 36% of the variables (scales) falling in the 0.1 hyperplane. The MAP test indicated 3 factors but this reduced the variables on the 0.1 hyperplane to 31%. The factor pattern matrix is summarized in Table 1. Of particular interest here is the close resemblance between this solution and those reported by Boyle (1987b, 1989a) for the SSQ factors. There are only two minor inconsistencies across the three studies concerning the Depression and Regression factors. Noticeably negative affect is found alongside the Anxiety factor and positive affect is on the same factor as Extraversion, as found by Cooper and McConville (1989). A principal axis factoring was also carried out on these data (the preferred method in Boyle’s work). Although four factors accounted for 79.3% of the scale variance and placed 40% of the variables in the 0.1 hyperplane all the 8SQ scales tended to load together on the first factor with some spreading between factors. This appeared to have a much less meaningful structure than the preferred maximum likelihood solution. The similarity in scale alignment between the two solutions holds despite a major difference in methodology. Boyle advocates the use of a dR factor analysis, factoring the difference scores between two occasions. This is believed to overcome the possible “trait contamination” of responses from the more common snapshot R technique. However, Table 1 shows three similar solutions, regardless of methodology. Note that Boyle’s (1989a) reanalysis of a previous study also employs an R factoring (although his table heading refers to difference scores), with little departure from the present results.
CHRIS MCCONVILLE and C~LIN COOPER
914
Table 1. Factor analysis of 15 mood scales, with a comparison of the higher-order structure of the SSQ across three studies (in boxes) Present study (R factor analysis)
Factors
Factors 1st
3rd
2nd
4th
1st -
-0.47
Pos affect Neg affect
Boyle (1989a) (R factor analysis)
Boyle (1987b) (dR factor analysis)
Factors 1st
2nd
2nd
Anxiety stress Depression Regression Fatigue Guilt Extraversion Arousal Clearheaded Elated Confident Energetic Agreeable
Item factoring:
what are the main primary factors?
Having obtained some consistent results with past studies attention was turned to the identification of the main mood factors from the 170 mood items. A principal components analysis was performed on the items. Five factors were extracted as indicated by the Scree Test, and these were rotated using Direct Oblimin. The first 5 components accounted for 36.6% of the total item variance, with 40% of the variables falling in the 0.1 hyperplane. The MAP test indicated that 14 factors be extracted but this added very little to the interpretation. Increasing the number of factors from 5 to 14 increased the percentage of variance explained by only 12% (to 48.2%). As this tended to break the factors into smaller, less meaningful structures no further attention was given to this solution. All five factors were bipolar using both positively and negatively toned items in their structure. One factor was derived mainly from POMS items with a few from negative affect. This amounted to a factor of Depression. It contained all the depression adjectives from the POMS ElationDepression scale, but not from the elated end of the dimension (except the item ‘elated’). It also included many items relating to feelings of inadequacy and muddled thoughts, classic symptoms of depression (DSM-III, 1980). Another factor contained a collection of terms pertaining to Hostility with a negative usage of elation items, most items were from POMS scales. A third factor was made up of Energy-Fatigue terms from both the 8SQ and the POMS. An Anxiety factor was also present composed of a few negative affect items and many from the SSQ Anxiety, Stress, Depression, Guilt and Fatigue factors. The fifth factor loaded on some positive affect, 8SQ and POMS items. This could be termed an Extraversion factor, loading best on items relating to being adventurous and a liking for games and parties. Table 2 shows the factor correlation matrix with a range of correlations between 0.10 and 0.33. The five factors were then subjected to a second-order principal components analysis. Both the Scree Test and the MAP test indicated that one factor should be extracted. Figure 1 (the lines leading from each box to the second-order factor) shows that all five factors loaded fairly equally on this general factor (indicating a depressed, angry, tired, anxious and unsociable attribute), which could be described as a state of hedonic tone, the one positive factor related inversely to the other four. The first principal component accounted for 39.2% of the variance in the five primary factors.
Table 2. Factor correlation matrix for the five primary factors I 1. 2. 3. 4. 5.
Depression Hostility Fatigue Anxiety Extraversion
1.0 0.21 0.20 0.32 -0.10
2 1.0 0.33 0.25 -0.30
Factors 3
1.0 0.23 -0.30
4
1.0 -0.12
5
1.0
The structure
1
rt
Hostlllty
Depresslo” 4.44%
residual
3.20%
residual
of moods
Anxiety
Fatigue 3.15%
915
residual
3.10%
residual
Extroversion 3.28%
residwl
‘\\’
Fig. 1. Schematic representation of the mood hierarchy. The loadings of the five primary factors are shown projected on to the general factor. Also shown is the residual variance for each primary factor after removing the influence of the general factor.
The hierarchy:
are any primary factors
uncorrelated
with the general factor?
In the analysis up to this point only variance common to all the primaries has been extracted. The importance of the variance that has been discarded due to low correlations between the primaries and this general factor has yet to be determined. Partialling out the general factor from the five primaries makes it orthogonal to the remainder of the variance on each factor. This is important, because it is possible that the general factor could correlate very substantially with some primary factors but nearly zero with others. This would necessitate considering both the general factor and any primary factors that failed to load on it when assessing the broad dimensions of moods. Figure 1 indicates only minor independent effects for each of the 5 primary factors. Levels of residual variance ranged from 3.1% for anxiety to 4.4% for depression. The general factor absorbed 23.8% of the total variance for the five components originally extracted (36% was the original figure although rounding errors bring this to 40%, smaller data sets yield closer estimates). The place of positive
and negative
a#ect
in the structure
of moods
As a single ‘feeling-good’ vs ‘feeling-bad’ factor at the top of the hierarchy is not consistent with the two-factor theory this was investigated further by replicating a study by Cooper and McConville (1989). They used 41 items from state Anxiety, state Extraversion and positive and negative affect scales also used in the present study. Their Chain-P factor analysis found two main factors (state Anxiety and state Extraversion) that were essentially separable. The rationale for this replication concerns proper item and mood variable sampling. As only five factors have been uncovered at the primary level questions are raised about the independence of the supposedly distinct scales of the POMS and 8SQ-the former questionnaire purports to measure six moods and the latter eight. One possible reason for their apparent disintegration is that too many similar items were contained in scales with different names. The clinical nature of these terms was also reflected in the content of the individual primaries, with the final result being a single factor at the top of the hierarchy that was “swamped” by the anxiety types. This replication drastically cuts the number of items loading on the general factor to enable the state Extraversion and positive affect items to exert whatever influences they may have.
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A principal components analysis was performed with three components extracted to echo the previous analysis on these items (the MAP test and Scree Test both indicated 3 factors with these data). These were then subjected to Direct Oblimin rotation. The 3 components claimed 36.4% of item variance, with 32.5% of the variables falling in the 0.1 hyperplane. The resultant factors of Anxiety, Extraversion and Anger were identical to those found by Cooper and McConville (1989). Anxiety and Anger were highly correlated in both studies (r = 0.56 here and r = 0.60 in the earlier study). Unlike the previous study a hierarchical analysis was performed on the three factors. A general factor of state Neuroticism was found that took up most of the variance in the Anger and Anxiety primaries, accounting for 22.7% of the total 36.4%. Interestingly, a sizeable portion of variance (7%; which is approx. 20% of the total item variance) remained in the Extraversion factor, which retained several large loadings. DISCUSSION
The present study attempted to identify the main self-report mood factors and any important primary factors independent of the higher-order factors. The results showed five primary factors, each contributing to the higher-order structure. The main analysis uncovered a basic structure of self-report mood of a single feeling-good/ feeling-bad dimension. This is similar to the hedonic tone dimension popular among early mood theorists (e.g. Wessman & Ricks, 1966). Unfortunately, it does not agree with the current view that two orthogonal factors best explain the major factor space. However, the adequacy of this one-factor structure is questionable in the light of the post hoc 41-item analysis. Here the two-factor structure is found among a selection of the items. This latter analysis differed from the 170-item analysis by employing a more even distribution of state Anxiety and Extraversion items (negative and positive affect, respectively). Indeed, the hierarchical analysis of the 170 items revealed a dominant role for ‘anxious’ type items, suggesting that the one-factor result may be due to sampling effects. Although care was taken to attempt to cover a broad spectrum of mood terms, there may not have been enough positive affect items. Bipolar factors of dimensions such as Anxiety are common .but few tap the state of Sociability. This may explain how the 170 items formed a hierarchy topped by a single hedonic tone dimension. : ’ Supposedly distinct mood scales appear to contain too many similar items. Matthews (1983) has shown evidence for this by reporting high intercorrelations between the SSQ scales, as has the present study-the general factor was composed of a mixture of Stress, Depression, Anxiety and other factors. Similarly, the discriminant validity of some of the POMS scales is doubtful (Norcross, Guadagnoli & Prochaska, 1984). Obviously the clinical importance of Stress, Anxiety and Depression factors outweighs the usefulness of sociable (extraverted) states, biasing most scales toward these clinically observed concepts. The loadings on the general factor show it to be a hedonic tone dimension. Some of the best markers are: “I’m in a playful, joyful mood” (0.726), “I feel so down I wonder if I can make it through the day” (-0.725) “I’m in a nice, comfortable mood” (0.718), “At the moment I feel in tip-top shape, both mentally and physically” (0.697), “Active” (0.670), “Energetic” (0.652), “Gloomy” (-0.640), with the highest loadings, generally, from the 8SQ items. Several markers of the Watson and Tellegen and Russell factors were included here but neither factorial alternative could be clearly identified. Both sets of factors had items loading highly on the general factor, but equally both had some marker items that did not. Steps were taken at the onset of the analysis to prevent any state being over-represented, which led to the removal of one anxiety scale. This was useful at the primary level but evidently less so at the highest level. The more even distribution of items in the 41-item analysis indicated a clearer picture of self-reported mood. Here both the Anxiety and Extraversion states showed up clearly and distinctly. The present results are consistent with previous literature, the 41-item analysis and the scale factorings both satisfactorily replicated earlier findings. Similar second orders from the identically forced primary factors in this study and those of Boyle (1987b, 1989a) suggests that the results obtained with items would be found in other studies. It is also highly likely that the 8SQ factors were wrongly identified in the first instance. This is evident from the reports of high intercorrela-
The structure of moods
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tions between the eight scales as reported by Matthews (1983) but also by Boyle (1987a, Table 2). The five primary factors from the present results show much more moderate intercorrelations, thereby suggesting that some reported mood factors are less than perfectly discriminating. A possible criticism concerns the young age of the respondents in this study (mean age = 15.2). However, there is little to suggest that the range of scores from an adolescent sample does not approximate the distribution within the population. Lorr and Wunderlich (1988) administered the POMS to high school pupils whose average age was 15.6 and found a similar factor structure to that of adult samples. Similarly, Kotsch, Gerbing and Schwartz (1982) and Russell and Ridgeway (1983) used samples of children from various age groups with no departure from results of similar studies using adult samples. Therefore it is difficult to see how the present results would have been adversely affected by age. It might be argued that the items have been grouped together solely on their semantic relatedness. However, as the SSQ items are behaviourally anchored they would not necessarily follow this pattern, although adjectives might. It is more likely that the factors are valid, having used more than one item type. Additional evidence for the validity of the factors of positive and negative affect is given by Watson, Clark and Tellegen (1988). The questionable validity of the one-factor solution suggests that further work is needed to confidently map the higher-order mood sphere. Weeding out the redundant mood concepts will help to refine item samples. For instance, what is the difference between anxiety and stress? Evidently, very little difference can be determined (Matthews, 1983), yet scales exist attempting to differentiate between them. It has been suggested that mood scales do not suffer poor discriminant validity just because they happen to be highly correlated. Instead the preferred belief is that some moods occur together, as might be expected for negative states. Izard (1972) suggests that one mood elicits other related moods, which is why the high intercorrelations exist. If there were no correlations between moods higher-order analyses would not be possible and the mood sphere< would be mapped by the primary factors. Mood states are not reported in isolation and all’ have at least one other mood factor with which they correlate. Thus higher-order analys$ can be conducted to find the influence that produces this correlation (Cattell, 1973, p. 107’). If the intercorrelations are inflated through poor item sampling the resulting higher-orders.,may not be meaningful. The intercorrelations of the five primary factors in the present study are lower than others previously cited. These five factors loaded equally well on the general factor, indicating an important influence for this factor in self-reported mood. More sociability type items may have made a contribution to another higher-order factor as indicated by the 41-item analysis. Investigators need to be aware of their particular biases (clinical or otherwise) in choosing which moods to measure. Angleitner, John and Lohr (1986) pinpointed “borrowing” (p. 66) of items from earlier scales and the “idiosyncratic preconceptions” (p. 67) of researchers as recipes for seriously defective scales: defects, they stress, that cannot be repaired by factor or item analysis. The most likely outcome is highly homogeneous, bloated specific, scales, and scales that are simply too alike to be of any value. If major distinctions exist between states then they should be measured accurately. It is imperative that thorough sampling is carried out, to echo Kline (1986) “a test can be no better than its items” (p. 24).
CONCLUSIONS (1) From questionnaire measures of mood five primary factors were. found necessary to map affective space. These are Depression, Hostility, Energy, Anxiety and Extraversion. (2) One higher-order factor of hedonic tone (good vs bad mood) was found to influence all the primary factors. (3) A further analysis of a subset of items revealed a possible second orthogonal second-order factor of state Extraversion. This suggested that a two-factor structure of mood may be the most parsimonious account. Poor variable samples are proposed as a’major flaw in the currently available mood instruments.
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